Search Results for author: Michael Cogswell

Found 14 papers, 5 papers with code

Trigger Hunting with a Topological Prior for Trojan Detection

no code implementations ICLR 2022 Xiaoling Hu, Xiao Lin, Michael Cogswell, Yi Yao, Susmit Jha, Chao Chen

Despite their success and popularity, deep neural networks (DNNs) are vulnerable when facing backdoor attacks.

Improving Users' Mental Model with Attention-directed Counterfactual Edits

no code implementations13 Oct 2021 Kamran Alipour, Arijit Ray, Xiao Lin, Michael Cogswell, Jurgen P. Schulze, Yi Yao, Giedrius T. Burachas

In the domain of Visual Question Answering (VQA), studies have shown improvement in users' mental model of the VQA system when they are exposed to examples of how these systems answer certain Image-Question (IQ) pairs.

Question Answering Visual Question Answering

Emergence of Compositional Language with Deep Generational Transmission

1 code implementation ICLR 2020 Michael Cogswell, Jiasen Lu, Stefan Lee, Devi Parikh, Dhruv Batra

In this paper, we introduce these cultural evolutionary dynamics into language emergence by periodically replacing agents in a population to create a knowledge gap, implicitly inducing cultural transmission of language.

reinforcement-learning

Grad-CAM: Why did you say that?

2 code implementations22 Nov 2016 Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, Dhruv Batra

We propose a technique for making Convolutional Neural Network (CNN)-based models more transparent by visualizing input regions that are 'important' for predictions -- or visual explanations.

Image Captioning Visual Question Answering

Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

20 code implementations7 Oct 2016 Ashwin K. Vijayakumar, Michael Cogswell, Ramprasath R. Selvaraju, Qing Sun, Stefan Lee, David Crandall, Dhruv Batra

We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.

Image Captioning Machine Translation +3

Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles

no code implementations NeurIPS 2016 Stefan Lee, Senthil Purushwalkam, Michael Cogswell, Viresh Ranjan, David Crandall, Dhruv Batra

Many practical perception systems exist within larger processes that include interactions with users or additional components capable of evaluating the quality of predicted solutions.

Multiple-choice

Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

no code implementations19 Nov 2015 Stefan Lee, Senthil Purushwalkam, Michael Cogswell, David Crandall, Dhruv Batra

Convolutional Neural Networks have achieved state-of-the-art performance on a wide range of tasks.

Combining the Best of Graphical Models and ConvNets for Semantic Segmentation

no code implementations14 Dec 2014 Michael Cogswell, Xiao Lin, Senthil Purushwalkam, Dhruv Batra

We present a two-module approach to semantic segmentation that incorporates Convolutional Networks (CNNs) and Graphical Models.

Semantic Segmentation

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